Wireless based sensor networks contain sensors for environment monitoring but have restricted resources. Many clustering protocols are designed to prolong network lifetime but have problems of inadequate cluster head selection criteria, fixed clustering, and static rounds which consume more energy. It is needed to develop an adaptive clustering strategy for better CH selection and load balancing. In this article, we introduced an energy-efficient mobility based cluster head selection mechanism to overcome these limitations. CH selection is based on dedicated parameters that have a huge impact on the sensor energy consumption. The weightage of each node is calculated on the base of the node's mobility level, residual energy, distance to sink, and density of neighbors. Inter-cluster communication uses single-hop/multi-hop. MATLAB is used to perform simulations. Results show that the proposed approach EEMCS performs better as compared to the existing algorithms CRPD, LEACH, and MODLEACH in terms of load balancing, network stability, energy depletion, and throughput. Energy utilization in the case of EEMCS is much less and the network lifetime is greater than other existing protocols.
Internet of Drones (IoD) is a decentralized networking architecture that makes use of the internet for uniting drones to enter controlled airspace in a coordinated manner. On the one hand, this new clan of interconnected drones has ushered in a new era of real-world applications; Small drones, on the other hand, are generally not designed with security in mind, making them exposed to fundamental security and privacy concerns. Limited computing capabilities, along with communication over an open wireless channel, exacerbate these challenges, making the IoD unfeasible for secure operations. In this article, we propose an identity-based proxy signcryption scheme to address these issues. During data transfer between drones and to the cloud server, the proposed scheme supports outsourcing decryption and member revocation. The proposed scheme is based on the notion of Hyper Elliptic Curve Cryptography (HECC), which improves network computation efficiency. We use formal security analysis with the Random Oracle Model (ROM) to evaluate security toughness. The performance analysis of the proposed scheme has also been reviewed in terms of computation and communication costs with the relevant existing schemes. The results obtained from both the security and performance analyses affirm the superiority of the proposed scheme.
Brain Hemorrhage is the eruption of the brain arteries due to high blood pressure or blood clotting that could be a cause of traumatic injury or death. It is the medical emergency in which a doctor also need years of experience to immediately diagnose the region of the internal bleeding before starting the treatment. In this study, the deep learning models Convolutional Neural Network (CNN), hybrid models CNN+LSTM and CNN+GRU are proposed for the Brain Hemorrhage classification. The 200 head CT scan images dataset is used to boost the accuracy rate and computational power of the deep learning models. The major aim of this study is to use the abstraction power of deep learning on a set of fewer images because in most crucial cases extensive datasets are not available on the spot. The image augmentation and imbalancing the dataset methods are adopted with CNN model to design a unique architecture and named as Brain Hemorrhage Classification based on Neural Network (BHCNet). The performance of the proposed approach are analyzed in terms of accuracy, precision, sensitivity, specificity and F1-score. Further, the experimental results are evaluated by comparative analyses of the balanced and imbalanced dataset with CNN, CNN+LSTM and CNN+GRU models. The promising results are achieved with CNN by imbalancing the dataset and gain highest accuracy that outperforms the hybrid CNN+LSTM and CNN+GRU models. The results reveals the effectiveness of the proposed model for accurate prediction to save the life of the patient in the meantime and fast employment in the real life scenario.
Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.
Purpose: To develop semi-automated application software that quickly analyzes infrared meibography images taken with the CSO Sirius Topographer (CSO, Italy) and to compare them to the manual analysis system on the device (Phoenix software platform). Methods: A total of 52 meibography images verified as high quality were used and analyzed through manual and semi-automated meibomian gland (MG) detector software in this study. For the manual method, an experienced researcher circumscribed the MGs by putting dots around grape-like clusters in a predetermined rectangular area, and Phoenix software measured the MG loss area by percentage, which took around 10 to 15 minutes. MG loss was graded from 1 (<25%) to 4 (severe >75%). For the semi-automated method, 2 blind physicians (I and II) determined the area to be masked by putting 5 to 6 dots on the raw images and measured the MG loss area using the newly developed semi-automated MG detector application software in less than 1 minute. Semi-automated measurements were repeated 3 times on different days, and the results were evaluated using paired-sample t test, Bland–Altman, and kappa κ analysis. Results: The mean MG loss area was 37.24% with the manual analysis and 40.09%, 37.89%, and 40.08% in the first, second, and third runs with the semi-automated analysis (P < 0.05). Manual analysis scores showed a remarkable correlation with the semi-automated analysis performed by 2 operators (r = 0.950 and r = 0.959, respectively) (P < 0.001). According to Bland–Altman analysis, the 95% limits of agreement between manual analysis and semi-automated analysis by operator I were between −10.69% and 5% [concordance correlation coefficient (CCC) = 0.912] and between −9.97% and 4.3% (CCC = 0.923) for operator II. The limit of interoperator agreement in semi-automated analysis was between −4.89% and 4.92% (CCC = 0.973). There was good to very good agreement in grading between manual and semi-automated analysis results (κ 0.76–0.84) and very good interoperator agreement with semi-automated software (κ 0.91) (P < 0.001). Conclusions: For the manual analysis of meibography images, around one hundred dots have to be put around grape-like clusters to determine the MGs, which makes the process too long and prone to errors. The newly developed semi-automated software is a highly reproducible, practical, and faster method to analyze infrared meibography images with excellent correlation with the manual analysis.
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